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Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder()

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with...

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Autores principales: Sun, Zhiyuan, Yuan, Yunhao, Dong, Xiaoxiao, Liu, Zhimei, Cai, Kelong, Cheng, Wei, Wu, Jingjing, Qiao, Zhiyuan, Chen, Aiguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Asociacion Espanola de Psicologia Conductual 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498172/
https://www.ncbi.nlm.nih.gov/pubmed/37711468
http://dx.doi.org/10.1016/j.ijchp.2023.100409
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author Sun, Zhiyuan
Yuan, Yunhao
Dong, Xiaoxiao
Liu, Zhimei
Cai, Kelong
Cheng, Wei
Wu, Jingjing
Qiao, Zhiyuan
Chen, Aiguo
author_facet Sun, Zhiyuan
Yuan, Yunhao
Dong, Xiaoxiao
Liu, Zhimei
Cai, Kelong
Cheng, Wei
Wu, Jingjing
Qiao, Zhiyuan
Chen, Aiguo
author_sort Sun, Zhiyuan
collection PubMed
description The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.
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spelling pubmed-104981722023-09-14 Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder() Sun, Zhiyuan Yuan, Yunhao Dong, Xiaoxiao Liu, Zhimei Cai, Kelong Cheng, Wei Wu, Jingjing Qiao, Zhiyuan Chen, Aiguo Int J Clin Health Psychol Original Article The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children. Asociacion Espanola de Psicologia Conductual 2023 2023-09-07 /pmc/articles/PMC10498172/ /pubmed/37711468 http://dx.doi.org/10.1016/j.ijchp.2023.100409 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Sun, Zhiyuan
Yuan, Yunhao
Dong, Xiaoxiao
Liu, Zhimei
Cai, Kelong
Cheng, Wei
Wu, Jingjing
Qiao, Zhiyuan
Chen, Aiguo
Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder()
title Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder()
title_full Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder()
title_fullStr Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder()
title_full_unstemmed Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder()
title_short Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder()
title_sort supervised machine learning: a new method to predict the outcomes following exercise intervention in children with autism spectrum disorder()
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10498172/
https://www.ncbi.nlm.nih.gov/pubmed/37711468
http://dx.doi.org/10.1016/j.ijchp.2023.100409
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